class_docstr: OCOSRT: Observation-Centric SORT: Rethinking SORT for Robust
  Multi-Object Tracking
  
  This multi object tracker is the implementation of `OC-SORT
  <https://arxiv.org/abs/2203.14360>`_.
  
  Args:
      detector (dict): Configuration of detector. Defaults to None.
      tracker (dict): Configuration of tracker. Defaults to None.
      motion (dict): Configuration of motion. Defaults to None.
      init_cfg (dict): Configuration of initialization. Defaults to None.
docstr: Calculate losses from a batch of inputs and data samples.
docstr_tok: ["Calculate","losses","from","a","batch","of","inputs","and","data","samples","."]
code: def loss(
      self, inputs: Tensor, data_samples: TrackSampleList, **kwargs
  ) -> dict:
      """Calculate losses from a batch of inputs and data samples."""
      return self.detector.loss(inputs, data_samples, **kwargs)
  
file_path: mmdetection/mmdet/models/mot/ocsort.py
imports: from typing import Dict, Optional
  from torch import Tensor
  from mmdet.registry import MODELS
  from mmdet.structures import TrackSampleList
  from mmdet.utils import OptConfigType, OptMultiConfig
  from .base import BaseMOTModel
label_desc: Use image node_id 2 for calling the OCSORT obj's underlying member method code with example usage: obj.loss(inputs, data_samples) and returns: self